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程序目的： 输入一个任意大小的迷宫，用栈求出一条走出迷宫的路径，并 显示在屏幕上。 程序实现： 可以实现载入迷宫和保存迷宫，附带文件中有4个测试迷宫路径的 文件test1~4.dd。请将这些文件拷贝到TC当前目录下，或者在载 入时写明完全路径。由于屏幕大小的限制，当用户自己输入迷宫 时一定要注意：迷宫大小是有限制的，不小于4*3,不大于30*20。 否则会出现错误信息。输入开始时全是墙，用上下左右键移动， 用Del键删除墙，形成通路，用Enter键添加墙。输入结束时可以 将迷宫保存下来，以dd为扩展名。输入完毕时用F9键来得到结果， 找到路径时，屏幕下方会出现Path found，否则出现Path not found。 程序经Turbo C 2.0编译调试成功。运行时不用添加任何运行库。 不可以在VC上编译。-procedures purpose : to bring in a maze of arbitrary size, with a stack get out of the maze of trails and on the screen. Program : can achieve and preserve the printed maze maze, the accompanying documents were four test maze of trails to document test1 4.dd. I would be grateful if those files are copied to the TC current directory, or in the state when fully contained path. Due to restrictions on the size of the screen when the user input labyrinth must pay attention to : the size of the maze is a restriction, not less than 4* 3, not more than 30* 20. Otherwise there will be a wrong message. At the beginning of the importation of a

说明：  CC算法应用关联积分能够同时估计出时间延迟和嵌入维数，是相空间重构的前提。 本程序通过C-C算法计算duffing方程产生的混沌时间序列的时间延迟和嵌入维数。(CC algorithm can estimate both time delay and embedding dimension by using correlation integral, which is the premise of phase space reconstruction. This program uses C-C algorithm to calculate the time delay and embedding dimension of the chaotic time series generated by Duffing equation.)

源程序及详细程序实验报告-source and detailed experimental procedures report

auto movie url capture

说明：  使用python语言呢实现 图像添加水印和去除水印(Adding and removing watermarks from images)

可以实现图片立体式展示，并且支持鼠标滑动滚动操作(It can realize stereoscopic display of pictures and support mouse sliding and scrolling operation.)

说明：  有数据，能运行，亲测可以运行，下面是凑字数，用过都说好(Able to run DBN program)

说明：  模式识别解决各类问题，代码详解，可修改。(Pattern recognition solves all kinds of problems. The code is detailed and can be modified.)

一个非常值得研究的源代码，界面和功能几乎和windows自带的扫雷游戏一模一样，可以设置游戏难度等级，值得学习。-a very worthwhile study the source code, interface and functions almost windows own mine same game, the game can set the difficulty level, it is worth learning.

说明：  用于大数据分析及图像可视化应用，是最好的工具(Application of Large Data Analysis and Image Visualization)